Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [2]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[3]:
<matplotlib.image.AxesImage at 0x7fe69cb55550>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[4]:
<matplotlib.image.AxesImage at 0x7fe69cb064a8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [5]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [6]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    print(image_width)
    print(image_height)
    print(image_channels)
    print(z_dim)


    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    
    # TODO: Implement Function
    input_real = tf.placeholder(dtype=tf.float32, shape=(None, image_width, image_height, image_channels),name='input_real')
    input_z = tf.placeholder(dtype=tf.float32, shape=(None, z_dim), name='input_z')
    learning_rate = tf.placeholder(dtype=tf.float16, name='learning_rate')
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
28
28
3
100
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [7]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        # TODO: Implement Function
        # Conv1 - 28x28x3
        conv1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        #batch_norm1 = tf.layers.batch_normalization(conv1, training=True)
        leaky_relu1 = tf.maximum(alpha*conv1, conv1) 
        
        # Conv2 - 14x14x128
        conv2 = tf.layers.conv2d(leaky_relu1, 128, 5, strides=2, padding='same')
        batch_norm2 = tf.layers.batch_normalization(conv2, training=True)
        leaky_relu2 = tf.maximum(alpha*batch_norm2, batch_norm2) 

        # Conv3 - 7x7x256
        conv3 = tf.layers.conv2d(leaky_relu2, 256, 5, strides=1, padding='same')
        batch_norm3 = tf.layers.batch_normalization(conv3, training=True)
        leaky_relu3 = tf.maximum(alpha*batch_norm3, batch_norm3) 

#         # Conv3 - 3x3x512
#         conv4 = tf.layers.conv2d(inputs=leaky_relu3, filters=1024, kernel_size=5, strides=2, padding='same', activation=None)
#         batch_norm3 = tf.layers.batch_normalization(conv3, training=True)
#         leaky_relu3 = tf.maximum(alpha*batch_norm3, batch_norm3) 
     
        # 1x1x1024
        flat = tf.reshape(leaky_relu3, (-1, 7*7*256))
        logits = tf.layers.dense(flat, 1, activation=None)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
##### import problem_unittests


def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """        
    with tf.variable_scope('generator', reuse= not is_train ):
        x = tf.layers.dense(z, 7*7*256, activation=None)
        # Reshape 1 dimentional array into 4x4x512 for each input
        x = tf.reshape(x, (-1, 7, 7, 256))
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha*x, x)
            
        # conv2d_transpose1 into 7x7x256
        conv1_t = tf.layers.conv2d_transpose(x, 
                                             128, 
                                             5, 
                                             strides=1,
                                             padding='same')
        # 14x14x512
        conv1_t_bn = tf.layers.batch_normalization(conv1_t, training=is_train)
        conv1_t_relu = tf.maximum(alpha*conv1_t_bn, conv1_t_bn)
        
        
        # conv2d_transpose2 into 14x14x128
        conv2_t = tf.layers.conv2d_transpose(conv1_t_relu, 
                                             128, 
                                             5, 
                                             strides=2, 
                                             padding='same')
        conv2_t_bn = tf.layers.batch_normalization(conv2_t, training=is_train)
        conv2_t_relu = tf.maximum(alpha*conv2_t_bn, conv2_t_bn)
            
#         # conv2d_transpose1 into 12x12x128
#         conv3_t = tf.layers.conv2d_transpose(inputs=conv2_t_relu, 
#                                              filters=256, 
#                                              kernel_size=5, 
#                                              strides=2, 
#                                              padding='same', 
#                                              activation=None)
#         conv3_t_bn = tf.layers.batch_normalization(conv3_t, training=is_train)
#         conv3_t_relu = tf.maximum(alpha*conv3_t_bn, conv3_t_bn)
        
        # conv2d_transpose into 28 x 28 x out_channel_dim
        # tanh
        logits = tf.layers.conv2d_transpose(conv2_t_relu, 
                                            out_channel_dim, 
                                            5, 
                                            strides=2, 
                                            padding='same')
        out = tf.tanh(logits)
        return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    generator_out = generator(z=input_z, out_channel_dim=out_channel_dim, is_train=True, alpha=alpha)
    d_model_real, d_logits_real = discriminator(images=input_real, reuse=False, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(images=generator_out, reuse=True, alpha=alpha)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real))
    )
    
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake))
    )
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake))
    )
    
    d_loss = d_loss_real + d_loss_fake
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [25]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, print_every=10):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # Build Model
    #def model_inputs(image_width, image_height, image_channels, z_dim):

    input_real, input_z, lr = model_inputs(data_shape[1],
                                         data_shape[2],
                                         data_shape[3],
                                         z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3], 0.2)
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    
    saver = tf.train.Saver()
    #72 images
    sample_z = np.random.uniform(-1, 1, size=(72, *data_shape[1:]))

    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
                for batch_images in get_batches(batch_size):
                    batch_images = batch_images * 2
                    # TODO: Train Model
                    steps += 1
                     # Sample random noise for G
                    batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                     # Run optimizers
                    _ = sess.run(d_opt, feed_dict={input_real: batch_images, 
                                                   input_z: batch_z, 
                                                   lr:learning_rate})
                    _ = sess.run(g_opt, feed_dict={input_z: batch_z, 
                                                   input_real: batch_images,
                                                   lr:learning_rate})

                    if steps % print_every == 0:
                        # At the end of each epoch, get the losses and print them out
                        train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                        train_loss_g = g_loss.eval({input_z: batch_z})

                        print("Epoch {}/{}...".format(epoch_i+1, epochs),
                              "Discriminator Loss: {:.4f}...".format(train_loss_d),
                              "Generator Loss: {:.4f}".format(train_loss_g))
                        # Save losses to view after training
                        _ = show_generator_output(sess, 72, input_z, data_shape[3], data_image_mode)
        

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [26]:
batch_size = 200
z_dim = 100
learning_rate = 0.0001
beta1 = 0.05


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
print( mnist_dataset.shape)
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
(60000, 28, 28, 1)
28
28
1
100
Epoch 1/2... Discriminator Loss: 1.9477... Generator Loss: 0.1708
Epoch 1/2... Discriminator Loss: 2.3552... Generator Loss: 0.1174
Epoch 1/2... Discriminator Loss: 1.7716... Generator Loss: 0.2286
Epoch 1/2... Discriminator Loss: 0.9859... Generator Loss: 0.5716
Epoch 1/2... Discriminator Loss: 1.7045... Generator Loss: 0.2536
Epoch 1/2... Discriminator Loss: 1.3988... Generator Loss: 0.3601
Epoch 1/2... Discriminator Loss: 1.5197... Generator Loss: 0.3144
Epoch 1/2... Discriminator Loss: 1.4836... Generator Loss: 0.3280
Epoch 1/2... Discriminator Loss: 1.5402... Generator Loss: 0.3139
Epoch 1/2... Discriminator Loss: 1.5263... Generator Loss: 0.3249
Epoch 1/2... Discriminator Loss: 1.2870... Generator Loss: 0.4266
Epoch 1/2... Discriminator Loss: 1.3481... Generator Loss: 0.3893
Epoch 1/2... Discriminator Loss: 1.2027... Generator Loss: 0.4665
Epoch 1/2... Discriminator Loss: 1.3025... Generator Loss: 0.4049
Epoch 1/2... Discriminator Loss: 1.2396... Generator Loss: 0.4336
Epoch 1/2... Discriminator Loss: 1.1504... Generator Loss: 0.5075
Epoch 1/2... Discriminator Loss: 1.1433... Generator Loss: 0.4986
Epoch 1/2... Discriminator Loss: 1.1531... Generator Loss: 0.4888
Epoch 1/2... Discriminator Loss: 1.1563... Generator Loss: 0.4787
Epoch 1/2... Discriminator Loss: 1.0401... Generator Loss: 0.5699
Epoch 1/2... Discriminator Loss: 1.1683... Generator Loss: 0.4745
Epoch 1/2... Discriminator Loss: 1.0838... Generator Loss: 0.5328
Epoch 1/2... Discriminator Loss: 1.0865... Generator Loss: 0.5341
Epoch 1/2... Discriminator Loss: 0.9603... Generator Loss: 0.6333
Epoch 1/2... Discriminator Loss: 1.0409... Generator Loss: 0.5633
Epoch 1/2... Discriminator Loss: 1.0435... Generator Loss: 0.5509
Epoch 1/2... Discriminator Loss: 0.9360... Generator Loss: 0.6585
Epoch 1/2... Discriminator Loss: 0.9851... Generator Loss: 0.6003
Epoch 1/2... Discriminator Loss: 1.0361... Generator Loss: 0.5593
Epoch 1/2... Discriminator Loss: 0.9964... Generator Loss: 0.5949
Epoch 2/2... Discriminator Loss: 0.9713... Generator Loss: 0.5998
Epoch 2/2... Discriminator Loss: 0.9395... Generator Loss: 0.6523
Epoch 2/2... Discriminator Loss: 0.9337... Generator Loss: 0.6456
Epoch 2/2... Discriminator Loss: 0.9253... Generator Loss: 0.6595
Epoch 2/2... Discriminator Loss: 1.1646... Generator Loss: 0.4755
Epoch 2/2... Discriminator Loss: 0.9429... Generator Loss: 0.6495
Epoch 2/2... Discriminator Loss: 1.0451... Generator Loss: 0.5430
Epoch 2/2... Discriminator Loss: 0.8631... Generator Loss: 0.7449
Epoch 2/2... Discriminator Loss: 1.1036... Generator Loss: 0.5190
Epoch 2/2... Discriminator Loss: 1.0600... Generator Loss: 0.5541
Epoch 2/2... Discriminator Loss: 1.0934... Generator Loss: 0.5290
Epoch 2/2... Discriminator Loss: 1.1703... Generator Loss: 0.5047
Epoch 2/2... Discriminator Loss: 1.0030... Generator Loss: 0.6212
Epoch 2/2... Discriminator Loss: 1.2314... Generator Loss: 0.4505
Epoch 2/2... Discriminator Loss: 1.1318... Generator Loss: 0.5039
Epoch 2/2... Discriminator Loss: 1.1362... Generator Loss: 0.5129
Epoch 2/2... Discriminator Loss: 1.0442... Generator Loss: 0.5634
Epoch 2/2... Discriminator Loss: 1.0489... Generator Loss: 0.5672
Epoch 2/2... Discriminator Loss: 1.1204... Generator Loss: 0.5023
Epoch 2/2... Discriminator Loss: 1.1548... Generator Loss: 0.5033
Epoch 2/2... Discriminator Loss: 1.0723... Generator Loss: 0.5589
Epoch 2/2... Discriminator Loss: 1.0745... Generator Loss: 0.5582
Epoch 2/2... Discriminator Loss: 1.1379... Generator Loss: 0.5132
Epoch 2/2... Discriminator Loss: 0.9749... Generator Loss: 0.6297
Epoch 2/2... Discriminator Loss: 1.1026... Generator Loss: 0.5305
Epoch 2/2... Discriminator Loss: 1.1638... Generator Loss: 0.5059
Epoch 2/2... Discriminator Loss: 0.9397... Generator Loss: 0.7501
Epoch 2/2... Discriminator Loss: 1.1821... Generator Loss: 0.4775
Epoch 2/2... Discriminator Loss: 1.1099... Generator Loss: 0.5176
Epoch 2/2... Discriminator Loss: 1.1734... Generator Loss: 0.4816

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [27]:
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.05


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
28
28
3
100
Epoch 1/1... Discriminator Loss: 1.0564... Generator Loss: 1.7281
Epoch 1/1... Discriminator Loss: 1.5540... Generator Loss: 4.9447
Epoch 1/1... Discriminator Loss: 1.5810... Generator Loss: 4.3580
Epoch 1/1... Discriminator Loss: 1.9120... Generator Loss: 3.2320
Epoch 1/1... Discriminator Loss: 1.5807... Generator Loss: 2.6472
Epoch 1/1... Discriminator Loss: 1.9742... Generator Loss: 3.1057
Epoch 1/1... Discriminator Loss: 1.5144... Generator Loss: 1.8995
Epoch 1/1... Discriminator Loss: 2.0339... Generator Loss: 2.3236
Epoch 1/1... Discriminator Loss: 1.6282... Generator Loss: 1.9523
Epoch 1/1... Discriminator Loss: 1.6554... Generator Loss: 1.9728
Epoch 1/1... Discriminator Loss: 1.5240... Generator Loss: 1.6370
Epoch 1/1... Discriminator Loss: 1.2350... Generator Loss: 1.5998
Epoch 1/1... Discriminator Loss: 1.4207... Generator Loss: 1.5804
Epoch 1/1... Discriminator Loss: 1.6842... Generator Loss: 1.4197
Epoch 1/1... Discriminator Loss: 1.3820... Generator Loss: 1.4630
Epoch 1/1... Discriminator Loss: 1.4610... Generator Loss: 1.5191
Epoch 1/1... Discriminator Loss: 1.5817... Generator Loss: 1.4680
Epoch 1/1... Discriminator Loss: 1.2251... Generator Loss: 2.0169
Epoch 1/1... Discriminator Loss: 1.5046... Generator Loss: 1.8251
Epoch 1/1... Discriminator Loss: 1.5445... Generator Loss: 1.6418
Epoch 1/1... Discriminator Loss: 1.2912... Generator Loss: 1.5893
Epoch 1/1... Discriminator Loss: 1.2905... Generator Loss: 1.4609
Epoch 1/1... Discriminator Loss: 1.3107... Generator Loss: 1.7623
Epoch 1/1... Discriminator Loss: 1.0605... Generator Loss: 1.5390
Epoch 1/1... Discriminator Loss: 1.0183... Generator Loss: 1.8502
Epoch 1/1... Discriminator Loss: 0.8100... Generator Loss: 2.3283
Epoch 1/1... Discriminator Loss: 0.8055... Generator Loss: 1.8534
Epoch 1/1... Discriminator Loss: 1.3794... Generator Loss: 2.2973
Epoch 1/1... Discriminator Loss: 1.2590... Generator Loss: 1.5179
Epoch 1/1... Discriminator Loss: 1.3613... Generator Loss: 2.2109
Epoch 1/1... Discriminator Loss: 1.1277... Generator Loss: 2.0260
Epoch 1/1... Discriminator Loss: 1.1863... Generator Loss: 2.1370
Epoch 1/1... Discriminator Loss: 1.1849... Generator Loss: 1.7043
Epoch 1/1... Discriminator Loss: 1.5154... Generator Loss: 1.1684
Epoch 1/1... Discriminator Loss: 1.5841... Generator Loss: 2.7721
Epoch 1/1... Discriminator Loss: 1.2482... Generator Loss: 2.4974
Epoch 1/1... Discriminator Loss: 0.9762... Generator Loss: 2.4325
Epoch 1/1... Discriminator Loss: 1.2423... Generator Loss: 2.1968
Epoch 1/1... Discriminator Loss: 1.0715... Generator Loss: 2.0464
Epoch 1/1... Discriminator Loss: 0.8857... Generator Loss: 2.8773
Epoch 1/1... Discriminator Loss: 0.7869... Generator Loss: 2.3801
Epoch 1/1... Discriminator Loss: 1.1089... Generator Loss: 3.1233
Epoch 1/1... Discriminator Loss: 0.5762... Generator Loss: 1.8945
Epoch 1/1... Discriminator Loss: 0.9507... Generator Loss: 1.4714
Epoch 1/1... Discriminator Loss: 0.9161... Generator Loss: 0.6288
Epoch 1/1... Discriminator Loss: 0.5764... Generator Loss: 1.1692
Epoch 1/1... Discriminator Loss: 1.3709... Generator Loss: 0.3574
Epoch 1/1... Discriminator Loss: 1.1880... Generator Loss: 0.4875
Epoch 1/1... Discriminator Loss: 0.2872... Generator Loss: 1.9086
Epoch 1/1... Discriminator Loss: 0.6407... Generator Loss: 0.9984
Epoch 1/1... Discriminator Loss: 1.6303... Generator Loss: 1.7336
Epoch 1/1... Discriminator Loss: 1.1343... Generator Loss: 3.3691
Epoch 1/1... Discriminator Loss: 0.6232... Generator Loss: 3.3350
Epoch 1/1... Discriminator Loss: 1.1269... Generator Loss: 4.1911
Epoch 1/1... Discriminator Loss: 1.1099... Generator Loss: 3.6090
Epoch 1/1... Discriminator Loss: 0.3809... Generator Loss: 1.6589
Epoch 1/1... Discriminator Loss: 0.9851... Generator Loss: 0.5848
Epoch 1/1... Discriminator Loss: 1.2072... Generator Loss: 0.4566
Epoch 1/1... Discriminator Loss: 0.4604... Generator Loss: 1.4138
Epoch 1/1... Discriminator Loss: 1.1303... Generator Loss: 0.4924
Epoch 1/1... Discriminator Loss: 0.3149... Generator Loss: 1.9574
Epoch 1/1... Discriminator Loss: 1.1632... Generator Loss: 0.4856
Epoch 1/1... Discriminator Loss: 1.1920... Generator Loss: 0.4802
Epoch 1/1... Discriminator Loss: 0.5506... Generator Loss: 1.1004
Epoch 1/1... Discriminator Loss: 0.5698... Generator Loss: 1.1925
Epoch 1/1... Discriminator Loss: 1.0439... Generator Loss: 0.5335
Epoch 1/1... Discriminator Loss: 1.3550... Generator Loss: 0.3812
Epoch 1/1... Discriminator Loss: 0.5616... Generator Loss: 2.8317
Epoch 1/1... Discriminator Loss: 0.5281... Generator Loss: 3.2305
Epoch 1/1... Discriminator Loss: 0.6561... Generator Loss: 0.9636
Epoch 1/1... Discriminator Loss: 0.9519... Generator Loss: 0.6929
Epoch 1/1... Discriminator Loss: 0.2635... Generator Loss: 2.4060
Epoch 1/1... Discriminator Loss: 0.5151... Generator Loss: 3.6187
Epoch 1/1... Discriminator Loss: 2.1552... Generator Loss: 5.5665
Epoch 1/1... Discriminator Loss: 0.9605... Generator Loss: 1.7182
Epoch 1/1... Discriminator Loss: 0.6159... Generator Loss: 3.0026
Epoch 1/1... Discriminator Loss: 0.7124... Generator Loss: 0.9136
Epoch 1/1... Discriminator Loss: 1.3566... Generator Loss: 0.3807
Epoch 1/1... Discriminator Loss: 1.0313... Generator Loss: 4.8825
Epoch 1/1... Discriminator Loss: 1.0164... Generator Loss: 3.1972
Epoch 1/1... Discriminator Loss: 0.3792... Generator Loss: 1.6551
Epoch 1/1... Discriminator Loss: 1.2432... Generator Loss: 0.4186
Epoch 1/1... Discriminator Loss: 0.8438... Generator Loss: 0.7746
Epoch 1/1... Discriminator Loss: 1.1270... Generator Loss: 3.8487
Epoch 1/1... Discriminator Loss: 0.6025... Generator Loss: 4.0340
Epoch 1/1... Discriminator Loss: 1.6471... Generator Loss: 3.7116
Epoch 1/1... Discriminator Loss: 0.6369... Generator Loss: 3.7186
Epoch 1/1... Discriminator Loss: 0.3564... Generator Loss: 3.8896
Epoch 1/1... Discriminator Loss: 0.4116... Generator Loss: 3.5821
Epoch 1/1... Discriminator Loss: 0.4158... Generator Loss: 1.8895
Epoch 1/1... Discriminator Loss: 0.3975... Generator Loss: 1.7553
Epoch 1/1... Discriminator Loss: 0.3841... Generator Loss: 2.6321
Epoch 1/1... Discriminator Loss: 1.0065... Generator Loss: 0.5567
Epoch 1/1... Discriminator Loss: 0.4110... Generator Loss: 3.0461
Epoch 1/1... Discriminator Loss: 1.2330... Generator Loss: 3.2159
Epoch 1/1... Discriminator Loss: 0.5948... Generator Loss: 3.1631
Epoch 1/1... Discriminator Loss: 0.5838... Generator Loss: 1.0487
Epoch 1/1... Discriminator Loss: 0.3408... Generator Loss: 2.0905
Epoch 1/1... Discriminator Loss: 2.0403... Generator Loss: 1.9910
Epoch 1/1... Discriminator Loss: 0.3533... Generator Loss: 3.3405
Epoch 1/1... Discriminator Loss: 0.4309... Generator Loss: 2.9552
Epoch 1/1... Discriminator Loss: 0.6196... Generator Loss: 3.9575
Epoch 1/1... Discriminator Loss: 0.6631... Generator Loss: 2.6265
Epoch 1/1... Discriminator Loss: 0.8349... Generator Loss: 2.9307
Epoch 1/1... Discriminator Loss: 1.2089... Generator Loss: 3.6154
Epoch 1/1... Discriminator Loss: 0.8458... Generator Loss: 2.5986
Epoch 1/1... Discriminator Loss: 0.9885... Generator Loss: 4.2683
Epoch 1/1... Discriminator Loss: 0.5073... Generator Loss: 2.6085
Epoch 1/1... Discriminator Loss: 0.5060... Generator Loss: 2.5941
Epoch 1/1... Discriminator Loss: 1.3357... Generator Loss: 0.3891
Epoch 1/1... Discriminator Loss: 2.2399... Generator Loss: 0.1699
Epoch 1/1... Discriminator Loss: 0.5081... Generator Loss: 1.3790
Epoch 1/1... Discriminator Loss: 0.6982... Generator Loss: 0.9511
Epoch 1/1... Discriminator Loss: 1.1226... Generator Loss: 0.5017
Epoch 1/1... Discriminator Loss: 0.7314... Generator Loss: 0.8622
Epoch 1/1... Discriminator Loss: 0.3165... Generator Loss: 1.8862
Epoch 1/1... Discriminator Loss: 0.5337... Generator Loss: 1.1972
Epoch 1/1... Discriminator Loss: 0.4177... Generator Loss: 1.5541
Epoch 1/1... Discriminator Loss: 0.3457... Generator Loss: 3.2088
Epoch 1/1... Discriminator Loss: 0.7581... Generator Loss: 3.0404
Epoch 1/1... Discriminator Loss: 0.4660... Generator Loss: 1.3611
Epoch 1/1... Discriminator Loss: 0.7201... Generator Loss: 3.4259
Epoch 1/1... Discriminator Loss: 0.7505... Generator Loss: 0.8573
Epoch 1/1... Discriminator Loss: 0.2356... Generator Loss: 2.3780
Epoch 1/1... Discriminator Loss: 0.5149... Generator Loss: 1.2488
Epoch 1/1... Discriminator Loss: 0.6885... Generator Loss: 4.6609
Epoch 1/1... Discriminator Loss: 3.0161... Generator Loss: 3.1827
Epoch 1/1... Discriminator Loss: 0.7653... Generator Loss: 3.8744
Epoch 1/1... Discriminator Loss: 0.4746... Generator Loss: 2.5543
Epoch 1/1... Discriminator Loss: 0.2864... Generator Loss: 2.4237
Epoch 1/1... Discriminator Loss: 0.9908... Generator Loss: 3.7056
Epoch 1/1... Discriminator Loss: 0.4304... Generator Loss: 1.4460
Epoch 1/1... Discriminator Loss: 0.4485... Generator Loss: 1.3095
Epoch 1/1... Discriminator Loss: 1.6462... Generator Loss: 0.3105
Epoch 1/1... Discriminator Loss: 0.5299... Generator Loss: 1.2104
Epoch 1/1... Discriminator Loss: 0.3088... Generator Loss: 2.0328
Epoch 1/1... Discriminator Loss: 1.2644... Generator Loss: 0.4625
Epoch 1/1... Discriminator Loss: 0.3591... Generator Loss: 3.3867
Epoch 1/1... Discriminator Loss: 0.4511... Generator Loss: 3.2385
Epoch 1/1... Discriminator Loss: 0.5794... Generator Loss: 1.0659
Epoch 1/1... Discriminator Loss: 0.1656... Generator Loss: 3.0981
Epoch 1/1... Discriminator Loss: 0.3514... Generator Loss: 4.2213
Epoch 1/1... Discriminator Loss: 0.1552... Generator Loss: 3.9165
Epoch 1/1... Discriminator Loss: 0.3564... Generator Loss: 4.5778
Epoch 1/1... Discriminator Loss: 1.5539... Generator Loss: 3.4695
Epoch 1/1... Discriminator Loss: 1.3741... Generator Loss: 3.4067
Epoch 1/1... Discriminator Loss: 0.2520... Generator Loss: 2.4175
Epoch 1/1... Discriminator Loss: 0.7595... Generator Loss: 2.9616
Epoch 1/1... Discriminator Loss: 0.2840... Generator Loss: 2.9409
Epoch 1/1... Discriminator Loss: 1.1263... Generator Loss: 0.4809
Epoch 1/1... Discriminator Loss: 0.3866... Generator Loss: 1.6079
Epoch 1/1... Discriminator Loss: 0.5161... Generator Loss: 1.1666
Epoch 1/1... Discriminator Loss: 0.2285... Generator Loss: 2.8415
Epoch 1/1... Discriminator Loss: 1.0215... Generator Loss: 0.5627
Epoch 1/1... Discriminator Loss: 0.5962... Generator Loss: 1.0251
Epoch 1/1... Discriminator Loss: 0.3415... Generator Loss: 1.6085
Epoch 1/1... Discriminator Loss: 0.7976... Generator Loss: 0.7322
Epoch 1/1... Discriminator Loss: 0.6544... Generator Loss: 1.0324

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.